Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin

Abstract

This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which were acquired simultaneously. This dataset provided the range of spatial heterogeneities of land surface necessary for the study, which was quantified by the dispersion variance. The LST scaling problem was studied by comparing the remotely sensed LST products in two ways. First, the LST products calculated in the distributed method and the lumped method were compared. Second, the airborne and satellite-based LST products derived from the TES algorithm were compared. Four upscaling methods of LST were used in the process. A scaling correction methodology was developed based on the comparisons. The results showed that the scaling effect could be as large as 0.8 K when the spatial resolution of the TASI LST data was coarse. The scaling effect increases quickly with the spatial resolution until it reaches the characteristic scale of the landscape and is positively correlated with the spatial heterogeneity. The first two upscaling methods denoted as Methods 1-2 can upscale the LST more effectively when compared with the other two scaling methods (Methods 3-4). The scaling effect for the ASTER data is not notable. The comparison between the TASI and 6490ASTER data showed that they were highly consistent, with a root mean square error (RMSE) of approximately 0.88 K, when the pixels were relatively homogeneous. When the spatial heterogeneity was significant, the RMSE was as large as 2.68 K. The scaling correction methodology provided resolution-invariant results with scaling effects of less than 0.5 K.

Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong

“…Findings from the comparison of LSTs retrieved from Sentinel-3 SLSTR and Himawari-8 satellites using SWA with LSTs retrieved from Landsat-8 using both MWA and SWA revealed similarity in the spatial distribution pattern of resulting LSTs, of the LST maps from the three satellites irrespective of the retrieval algorithm. However, as suggested in the literature [54,55] and confirmed by this study, LSTs derived from the three satellite sensors are not directly compactible. The disparities in estimated LST between Himawari-8, Sentinel-3 SLSTR, and Landsat 8 are directly related to variances in the spectral bandwidth and radiometric resolution between the satellite sensors (see Figure 2).…”

“…The study demonstrates a higher correlation between SWA H8 and SWA S3 in the nighttime compared to daytime. The bias is less significant at nighttime, largely due to the lower dependency on differential surface cooling/heating at nighttime, and this makes nighttime LST more efficient for algorithm testing and temperature analysis [54]. In addition, SD during the daytime is relatively larger than at night.…”

Directional variation in surface emissivity inferred from the MYD21 product and its influence on estimated surface upwelling longwave radiation

“…An intrinsic property of LSE is its dependency on viewing zenith angle (VZA), which can be attributed to factors such as land surface composition (Garcí a-Santos et al 2012;Lagouarde et al 1995), roughness and structure (Snyder et al 1998), and spatial heterogeneity (Hu et al 2015;Ren et al 2014). Even for flat and homogeneous surfaces such as open water, the emissivity is VZA-dependent (Masuda et al 1988).…”

An Improved Computational Geometry Method for Obtaining Accurate Remotely Sensed Products via Convex Hulls With Dynamic Weights: A Case Study With Leaf Area Index

“…By extending the TSEMbased upscaling model, Garrigues et al effectively corrected for the scale effects of the multivariate retrieval function [12]. Consequently, the TSEM-based upscaling model has been applied in several studies to various surface parameters and has achieved satisfactory results [13][14][15][16]. However, it is rather difficult to obtain the textural parameters required by the TSEM-based upscaling model.…”